Verified65% confidenceFactExact time
The price of output tokens is typically 4–6 times that of input tokens, stemming from the computational differences between the prefill and decode phases, with GPU utilization during the decode phase potentially being only 10–20% of that during prefill
3
Sources
65%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
GPT-5.6 In-Depth Hands-On Review: How Do Soul, Tara, and Luna Really Perform?
bilibiliAI-seeker7/9/2026
Related Claims
VerifiedDuring the autoregressive decoding phase, GPU compute utilization is often less than 10%, making it a memory-intensive operation.77% similarUnverifiedOutput token pricing is typically 2–4 times that of input tokens, because the generation process requires step-by-step decoding and computation of attention weights.75% similarVerifiedThe price of output tokens for large language models is typically 4–8 times that of input tokens, because output involves autoregressive generation requiring multiple forward passes, resulting in higher marginal costs.73% similarUnverifiedInput tokens can be processed in parallel with high utilization; output tokens must be generated one by one autoregressively, occupying more memory bandwidth and being slower, so most providers set the output-side unit price significantly higher than the input side, with a common ratio of 3:1 to 5:1.72% similarUnverifiedOutput tokens are typically more expensive than input tokens because outputs require autoregressive token-by-token generation, which has higher computational intensity and lower GPU utilization.72% similar
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